Data Analysis with LLMs: Text, tables, images and sound

Speed up common data science tasks with AI assistants like ChatGPT and Large Language Models (LLMs) from Anthropic, Cohere, Open AI, Google, Hugging Face, and more!

Data Analysis with LLMs teaches you to use the new generation of AI assistants and Large Language Models (LLMs) to aid and accelerate common data science tasks.

Learn how to use LLMs to
Analyze text, tables, images, and audio files
Extract information from multi-modal data lakes
Classify, cluster, transform, and query multimodal data
Build natural language query interfaces over structured data sources
Use LangChain to build complex data analysis pipelines
Prompt engineering and model configuration

All practical, Data Analysis with LLMs takes you from your first prompts through advanced techniques like creating LLM-based agents for data analysis and fine-tuning existing models. You’ll learn how to extract data, build natural language query interfaces, and much more.

About the Technology
Large Language Models (LLMs) can streamline and accelerate almost any data science task. Master the techniques in this book, and you’ll be able to analyze large amounts of text, tabular and graph data, images, videos, and more with clear natural language prompts and a few lines of Python code.

About the Book
Data Analysis with LLMs shows you exactly how to integrate generative AI into your day-to-day work as a data scientist. In it, Cornell professor Immanuel Trummer guides you through a series of engaging projects that introduce OpenAI’s Python library, tools like LangChain and LlamaIndex, and LLMs from Anthropic, Cohere, and Hugging Face. As you go, you’ll use AI to query structured and unstructured data, analyze sound and images, and optimize the cost and quality of your data analysis process.

What's Inside
Classify, cluster, transform, and query multimodal data
Build natural language query interfaces over structured data sources
Create LLM-based agents for autonomous data analysis
Prompt engineering and model configuration